Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines
Summary: Biathlon, an ML-serving system, exploits model resilience to input perturbations by selecting per-aggregation-feature approximation levels to maximize latency reduction while guaranteeing bounded end-to-end accuracy loss. Evaluated on real pipelines, it achieves 5.3x–16.6x speedups with negligible accuracy drop. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chaokun Chang (Chinese University of Hong Kong)
- 2. Eric Lo (Chinese University of Hong Kong)
- 3. Chunxiao Ye (Chinese University of Hong Kong)
BibTeX Citation
@article{chang_vldb24,
title = {{Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines}},
author = {Chang, Chaokun and Lo, Eric and Ye, Chunxiao},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {10},
pages = {2631--2640},
doi = {10.14778/3675034.3675052},
url = {https://doi.org/10.14778/3675034.3675052},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,900 | Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving | 2025 | SIGMOD | 5.7430032e-05 |
| 10,623 | KEN: An Execution Engine for Unstructured Database Systems | 2026 | VLDB | 5.093636e-05 |
| 10,769 | Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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